System and method for customized user content

ABSTRACT

Systems, methods, and computer-readable storage media for how to select, suggest, and modify content, which is relevant to the user, on a user interface. The system does this by combining physiological data, location data, and historical data to create a multi-dimensional user state of the user, where at least one dimension is time. The system also identifies available content, ranks the available content based on the multi-dimensional user state, and transmits a user interface a suggestion for a top-ranked item within the ranked list of available content.

BACKGROUND 1. Technical Field

The present disclosure relates to customizing user content, and morespecifically to suggesting and modifying content based on a combinationof physiological and psychological data.

2. Introduction

Presenting and suggesting customized content to a user interfacerequires information about the user. Possibly the most common example atpresent are banner advertisements for websites, which often use a user'sprevious web-browsing history to provide specific content to that user.With wearable devices becoming more ubiquitous, user interfaces may varybased on an individual's given physiological condition, such as if theuser's heart beat is irregular. In some cases, a mental state of theuser is predicted and used to suggest specific content, suggestingdifferent content if the user is sad versus happy.

However, in all these cases, the content modifications and suggestionsare based on a current status of the user, rather than how the userstate has evolved over time, or how the user state has evolved withrespect to other factors or influences. To use an analogy, previousapproaches inadequately used a static view to measure something that isdynamically in motion. Such methods to determine user status, and makesubsequent modifications/suggestions to content on a user interfacebased on that user status, fail to account for why the user status haschanged.

TECHNICAL PROBLEM

How to program a computer system to select, suggest, and modify contentwhich is relevant to the user.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media a technical solution to the technical problem described. Amethod for performing the concepts disclosed herein can includereceiving, at a server from a sensor, physiological data captured by thesensor from a user; receiving, at the server from a computing device,location data of the user; sending a request to a historical databasefor historical user data for the user; receiving, from the historicaldatabase, the historical user data; generating, via a processor of theserver, and based on the physiological data, the location data, and thehistorical user data, a multi-dimensional user state of the user, themulti-dimensional user state having a plurality of dimensions, where atleast one dimension in the plurality of dimensions is time; sending,from the server to a content database, a request for a list of availablecontent; receiving, at the server from the content database, the list ofavailable content; ranking, via the processor of the server and usingthe multi-dimensional user state, content within the list of availablecontent based on a comparison to the multi-dimensional user state,resulting in a ranked list of available content; and transmitting, fromthe server to the computing device, a suggestion comprising at least atop-ranked item within the ranked list of available content.

A system configured to perform the concepts disclosed herein can includea processor; and a non-transitory computer-readable storage mediumhaving instructions stored which, when executed by the processor, causethe processor to perform operations comprising: receiving, from asensor, physiological data captured by the sensor from a user;receiving, from a computing device, location data of the user; sending arequest to a historical database for historical user data for the user;receiving, from the historical database, the historical user data;generating, based on the physiological data, the location data, and thehistorical user data, a multi-dimensional user state of the user, themulti-dimensional user state having a plurality of dimensions, where atleast one dimension in the plurality of dimensions is time; sending, toa content database, a request for a list of available content;receiving, from the content database, the list of available content;ranking content within the list of available content based on acomparison to the multi-dimensional user state, resulting in a rankedlist of available content; and transmitting, to the computing device, asuggestion comprising at least a top-ranked item within the ranked listof available content.

A non-transitory computer-readable storage medium configured asdisclosed herein can have instructions stored which, when executed by acomputing device, cause the computing device to perform operations whichinclude receiving quantified subjective data associated with a user;receiving, from a computing device, location data of the user; sending arequest to a historical database for historical user data for the user;receiving, from the historical database, the historical user data;generating, based on the quantified subjective data, the location data,and the historical user data, a multi-dimensional user state of theuser, the multi-dimensional user state having a plurality of dimensions,where at least one dimension in the plurality of dimensions is time;sending, to a content database, a request for a list of availablecontent; receiving, from the content database, the list of availablecontent; ranking content within the list of available content based on acomparison to the multi-dimensional user state, resulting in a rankedlist of available content; and transmitting, to the computing device, asuggestion comprising at least a top-ranked item within the ranked listof available content.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system embodiment;

FIG. 2 illustrates example multi-dimensional user statuses which changein reaction to a stimulus;

FIG. 3 illustrates example data which can form the basic structure of amulti-dimensional user status;

FIG. 4 illustrates an example of a user receiving content suggestions inan office environment;

FIG. 5 illustrates an example of a user receiving content suggestions ina home environment;

FIG. 6 illustrates an example method embodiment; and

FIG. 7 illustrates an example computer system.

DETAILED DESCRIPTION

Various embodiments of the disclosure are described in detail below.While specific implementations are described, it should be understoodthat this is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure.

The disclosed systems and methods allow computer systems to beprogrammed to select, suggest, and modify content onto a user interface.This can be accomplished, for example, by building a multi-dimensionalmodel of a user's user state, with at least one of the dimensions beingtime, and evaluating the user state changes and reactions to types ofstimuli. At any single moment in time, the multi-dimensional model hasat least 2 +dimensions, such that the multi-dimensional model with timeis a 3D+ (three-dimensional plus) model. The inputs to the system can beobjective (such as sensor inputs from cameras, heart rate monitors,smart watches, keyboard and/or mouse usage rates, touch screen usagedata, etc.) or subjective (such as user inputs indicating how the usercurrently feels, mentally, emotionally, and/or physically). Thesubjective inputs may from the user to which content is being directed,from members of a team to which the user belongs, people in acircumstances similar to that of the user, and/or from a supervisor(such as a coach feeling like her team isn't performing to theirpotential).

To identify content for a user using a multi-dimensional model of theuser state can require knowing (1) how the user has reacted to specificcontent and stimuli in the past, and (2) what the desired user state is.

Regarding point (1), systems configured according to this disclosure cancombine user data, such as physiological data, location data, andhistorical user data to generate a multi-dimensional model where atleast one variable is time. The model can be used to identify how theuser has reacted to specific content and stimuli in the past, with themodel identifying specific correlations between content/stimuli and theassociated effects on the user state. For example, the model may usehistorical data about previous user states to determine that afterfifteen minutes in a particular location, the user state changesdramatically. Similarly, the model may use historical data to determinethat when the user is presented a particular type of advertisement, theuser state changes from a positive mood to a negative mood, as evidencedby both objective and subjective evidence.

Based on this analysis and these correlations, the multi-dimensionaluser state can also predict what the user state will be when the user ispresented new content, or is placed into a different circumstance. Thatis, the multi-dimensional model can be used to predict, based on how theuser (or other users similar to the user) have reacted in the past. Inorder for the system to properly suggest or modify content for the user,the system also determines a recommended user state. Determination of arecommended user state (point (2) above) can be determined based on auser input, such as “I'm tired and I want to feel rested,” or “I'm angryand I want to feel happy.” The determination of desired user state canalso be made based on historical data. If, for example, the user haspreviously indicated that they are frustrated at a given time, and thesystem identified a particular keyboard strike rate at that time, whenthe system detects that strike rate again the system can determine thatthe user is again frustrated and seek to calm the user. Yet another waythe system can determine the desired user state is based on the desiredstates of similar users. If the user is identified as an optimist, thesystem can seek to adjust the user's user state based on how otheroptimists have reacted in similar circumstances. Likewise, if the useris an athlete, the system can seek to adjust the user's user state basedon how other athletes have reacted in similar circumstances.

The content which is suggested or modified to the user based on themulti-dimensional user state can be, for example, advertisements, music,movies/television/videos, podcasts, live streams, exercise suggestions,food suggestions, restaurant suggestions, travel route suggestions, etc.For example, if a user is watching television at home, and themulti-dimensional user state is used to predict that the user is in asad mood and wishes to feel happier, the system can suggest a particulartelevision show which has, in the past, moved the user from a sad moodto a happier mood. As another example, if the user is exercising, andthe multi-dimensional user state is used to predict that the user is“going through the motions” and not exercising to their potential, butwould like to exercise to their potential, the system can suggest aparticular song known to motivate the user. As yet another example, ifthe user is looking for food while driving home, the system may predict,based on the multi-dimensional user state, that the user would likelyprefer a fast-food restaurant over an upscale/sit-down restaurant. Inyet another example, the system can be configured to recommend contentfor the user based on professional growth and quality of life at work,and make content recommendations based on the multi-dimensional userstate of the individual. Exemplary data which can be used in such anexample can include body posture (slumping shoulders, rounded back),heart rate, HRV score, previously provided goals of the individual, aswell as subjective scores provided by management, peers, or theindividual themselves.

The content suggestions and/or modifications can be sent to, orotherwise modify, a user interface. For example, with a suggestion, ifthe user is interacting with a personal computer, tablet, smartwatch, orother computing device, the suggestions and/or modifications can be sentto the screen of that device, thereby allowing the user to see thesuggestions and select if they wish to modify the content based on thesuggestion. Likewise, if the system is modifying content based on themulti-dimensional user state (without asking for explicit permissionfrom a user to modify the content), the system may transmit the modifiedcontent directly to the screen of the user's device. For example, if thesystem identifies that the user, based on the user's multi-dimensionaluser state, would be a good match for a specific recommendation orspecific piece of content, the system may automatically present thatparticular recommendation or content to the user (on a user interface oraudibly). If the system is interacting with a coach or supervisor, thesystem may detect that particular content may be useful in changing theuser state of the entire group, and may ask the coach or supervisor forpermission to provide the content to the individual members of thegroup, or for permission to provide the content to the group as a whole.

The content which is available for suggestion can be, for example,stored in one or more databases. A database can be directly accessible,or can be accessible across a network, such as the Internet. The systemcan place a call, or request, to databases to identify what content isavailable, and in a response to the request can receive a list ofavailable content. The system can then rank that available content basedon the user's multi-dimensional user state (and the user state desired).The system can then modify or suggest content on the user interfacebased on the ranked list.

Content modifications or suggestions can, broadly, be segmented into twocategories (with potential overlap): activity content and influencecontent. Activity content are pieces of content which the user canconsume or participate in, such as an advertisement, a television show,a music suggestion, a workout suggestion, etc. In each of these cases,the user participates in an activity involving the selected content(even if that participation is only to decline a suggestion). Bycontrast, influence content is content which is selected with thepurpose of influencing the user's current state. For example, if theuser's multi-dimensional user state indicates the user is exhausted andangry because of a reason “A”, influence content “B” may be selected andsuggested to make the user happier in a specific manner. If the user'smulti-dimensional user state indicates the user is exhausted and sad forreason “X”, influence content “Y” may be selected to cheer the user up.

Within the multi-dimensional user state are relationships between thevarious stimuli, activities, content, and user statuses. While thespecifics of how the various pieces of data are inter-related can varyfrom configuration to configuration, the formation of themulti-dimensional user state can have: (1) elements which are specificto a given moment in time, such as a stimulus, the “static” state of theuser at that point in time, and user behavior at that point in time; and(2) elements which form relationships across the respective moments intime. In other words, the multi-dimensional user state is formed of datawhich is specific to a particular moment in time, as well asrelationships within the data between at least two different points intime. Preferably, the “static” user state has two or more data points ateach point in time, as well as one or more stimuli being presented tothe user at each point in time and the user's actions at each point intime. The multi-dimensional user state can also have, as part of the“static” values for a single moment in time, values based on acombination of the different static values. For example, within thestatic values for a singular moment in time can be one or more weightednumbers based on the individual static values.

In some cases, a multi-dimensional user state can be generated at asingle point in time based on multiple data inputs. Using this “static”multi-dimensional user state, the system can make recommendations basedon historical data for similar users according to age, race, gender,economic status, education, and/or other demographic data using thehistorical database. For example, upon generating a profile for a newuser, the new user will have no longitudinal data which the system canuse to make recommendations. Instead, the system can compare the user'smulti-dimensional user state to where in time or circumstance similarmulti-dimensional user states have been present among similarindividuals (such comparison can make use of a historical database ofmulti-dimensional user states). Based on that comparison, the system canmake a recommendation, despite not having data over time for thespecific individual.

Content on the user interface can be replaced and/or modified. In someinstances, this can take the form of a content suggestion beingtransmitted to and displayed on the user interface, at which point theuser can select if they wish to display the content. In other instances,content which is already being displayed on the user interface can bechanged or modified based on the multi-dimensional user state and theranked, available content. For example, if a recommendation for contentis being displayed on the user interface, the system can modify thatrecommendation, changing it to a new or updated recommendation. Asanother example, if a user is listening to music, and the systemdetermines (through analysis of the multi-dimensional user state) thatthe user's state should be shifted in some manner, the system cansuggest, through a notification on the user interface, a change in themusic which would affect the user state.

Examples of these and other configurations will be further discussed inconjunction with the figures provided.

FIG. 1 illustrates an example system embodiment 102. In this example, aserver 116 receives objective and/or subjective data from multiplesources 104-114. Environmental data 104 can be data identifying alocation, or a location type, of the user. For example, theenvironmental data can be location coordinates such as GPS (GlobalPositioning System) data or cellular triangulation information. Theenvironmental data can also identify if the user is in a kitchen, livingroom, office, park, or other descriptive type for the location.

The server 116 can also receive sensor data 106, examples of which caninclude physiological sensors (such as sensors on a smartwatch), cameraswhich record how the user behaves and reacts, data from a keyboard ormouse (such as how fast the user is typing, moving the mouse, etc.),etc. Subjective data 108 received by the server 116 can include datawhich the user themselves enter, such as how they feel (tired,energized, sad, happy, angry, etc.), or which can be entered by a thirdparty expert, such as a doctor, therapist, supervisor, or manager.

Historical data 110 can include information regarding what stimuli theuser has been previously exposed to and/or how the user reacted to thoseprevious stimuli. The historical data 110 can also include informationabout previous environmental data 104, sensor data 106, and/orsubjective data 108. The previous user states 112 can include previouslygenerated or identified user states. In some configurations, this can beone or more previous multi-dimensional user states, where at least oneof the dimensions is time, whereas in other configurations the previoususer states can be for single points in time. The userprofile/classification 114 can be a determined profile, group, or classof the user. Exemplary classifications can be “optimist,” “pessimist,”“athlete,” “mother,” “father,” “easily angered,” “country musicenthusiast,” etc.

The server 116 receives the inputs 104-114 and, using those inputs,generates a multi-dimensional user state. The server 116 also identifiesavailable content 118. The available content 118, as illustrated, iscontained in a database across a network 120, such as the Internet. Inother configurations, the available content 118 can be stored in adatabase within the server 116. Upon generating the multi-dimensionaluser state and identifying the available content 118, the server 116identifies which content, of the available content, should be suggestedto the user, and transmits that selected content to the user interfaceof the user's computing device. Exemplary user interfaces of computingdevices can include a monitor for a computer 122, the screen of asmartphone 124, and the screen of a smartphone 126 being worn by theuser. As the server 116 determines that the content being displayedshould change, the server 116 transmits updates or suggestions to theuser interfaces of the respective devices 122, 124, 126, where thesuggestions or updates are displayed.

FIG. 2 illustrates example multi-dimensional user statuses which changein reaction to a stimulus. In this example, two identicalmulti-dimensional user statuses 202, 204 for a single moment in time areillustrated, with one 202 corresponding to person A and another 204corresponding to person B. In this example, at the time illustrated, themulti-dimensional user statuses are three dimensional (3D), and identifyvalues and relationships which help quantify the user state of eachrespective user (person A and person B). In this example, the userstates 202, 204 are identical to illustrate that despite having similaruser states, when exposed to a stimulus X 206, the user states 202, 204change in distinct ways. For example, the person A user state changesfrom the initial user state 202 into user state 208, and the person Buser state changes from the initial user state 204 into user state 210,despite the respective user being exposed to the same stimulus 206. Thereasons for the distinct reactions (202 to 208 and 204 to 210) to thestimulus 206 can be determined using a multi-dimensional user state overtime, such that the system can predict how the users will react tospecific stimuli, or content, and make suggestions based on thatprediction.

FIG. 3 illustrates example data which can form the basic structure of amulti-dimensional user status. One piece of data can be a stimulus list302 which lists what stimuli the user has been exposed to at variouspoints in time. The stimulus list 302 can include multimedia contentwhich is being displayed on the user interface, other multimedia contentseen by the user, interactions with other people, or any other typecontent which can be considered “influence content.”

Another type of data stored is a record of multi-dimensional user states304 previously identified at specific points in time. As illustrated,each of the multi-dimensional user states 304 is, at a specific momentin time, three dimensional. However, in practice the multi-dimensionaluser states 304 at specific moments in time can be less than three(e.g., two) dimensions, or can have more than three dimensions. Inaddition, the multi-dimensional user states 304 can include weightedresults for a single point in time, where such weighted results areweighted based on two or more of the values used to formmulti-dimensional user state at a given point in time.

Yet another type of data stored can be the user behavior 306, indicatinghow the user is physically behaving or reacting at any given point intime. This can include how fast the user is reacting to contentdisplayed on a user device, emotions captured in a camera feed (andanalyzed using image processing), heart rate during the presentation ofspecific content, etc.

Finally, the multi-dimensional user state can further includerelationships between the various factors 302, 304, 306 over time. Forexample, the system can identify that the user state reverts to abaseline value after being presented certain content, or after the userbehaves in a certain way. This identified relationship, which extendsover time between pieces of content, can be stored as part of themulti-dimensional user state.

FIG. 4 illustrates an example 402 of a user 404 receiving contentsuggestions in an office environment. In this example, a user 404 iswearing a smartwatch 406 while working at a computer 408 with a mouse412 and keyboard 410. As the user 404 is working, her smartwatch 406 istransmitting her heartbeat 420 to a server 414. At the same time, as theuser 404 is working, the keystroke rate and mouse movement aretransmitted 422 from the computer 408 to the server 414. The server 414interacts with a historical database 416 to obtain past data regardingprevious user states, past interactions, previously viewed content, etc.The server 414 also interacts with a content database 418 to identifywhat content is currently available. Using, for example, the heartrate420, the keystroke rate/mouse movement 422, data from the historicaldatabase 416, the server 414 generates a multi-dimensional user state,where at least one of the dimensions is time. The server 414 then ranksthe available content from the content database 418, and transmits acontent suggestion or modification to the screen of the computer system408. In some cases, rather than transmitting the content suggestion ormodification to the screen of the computer system 408, the server 414can transmit the content suggestion or modification to the userinterface of the smartwatch 406 being worn by the user.

While the example illustrated in FIG. 4 communicates physiological data,a heartbeat 420, to the server 414 for analysis, in other configurationsthe data transmitted can be physiological, behavioral, psychological,subjective, and/or objective. In other words, if the data can bequantified, it can be transmitted to the server 414 for analysis.

FIG. 5 illustrates an example of a user 502 receiving contentsuggestions in a home environment. In this example, the user 502 iswatching television 506 at home while using a smartphone 504. Thetelevision 506 is transmitting 514 information to a server 508 about thecontent being viewed by the user 502, which can include shows and/oradvertisements being displayed. The smartphone 504 is also transmitting516 information to the server 508, such as, for example, whatadvertisements (such as banner advertisements) are being displayed onthe smartphone 504, what games are being played, tactile informationabout how the user 502 is interacting with the touch screen of thesmartphone 504, images from the camera of the smartphone 504, etc. Theserver 508 interacts with a historical database 510 to obtain past dataregarding previous user states, past interactions, previously viewedcontent, etc. The server 508 also interacts with a content database 512to identify what content is currently available. Using, for example, thedata from the smartphone 516, the data from the television 514, and thedata from historical database 510, the server 508 generates amulti-dimensional user state, where at least one of the dimensions istime. In other configurations, the multi-dimensional user state caninclude subjective inputs as well. The server 508 then ranks theavailable content from the content database 512, and transmits a contentsuggestion or modification 522, 524 to the screen of the television 506and/or to the user interface of the smartphone 504.

FIG. 6 illustrates an example method embodiment. The steps outlinedherein are exemplary and can be implemented in any combination thereof,including combinations that exclude, add, or modify certain steps. Theexample system provided, a server, can be replaced with any othercomputing system configured according to the principles disclosedherein. In this example, the server receives, from a sensor,physiological data captured by the sensor from a user (602). In someembodiments, the sensor can be a component of a wearable device, such asa smartwatch, heart rate monitor, etc., and the physiological data caninclude a heartbeat of the user captured by the smartwatch. In otherembodiments, the sensor can be a camera, and the physiological data caninclude facial movement of the user captured by the camera. In anotherexample, the sensor can be an accelerometer which detects how fast theuser is moving (or more precisely, starting and stopping). In yet otherembodiments, the system can use a combination of multiple sensors todetect the physiological data of the user, such as a smartwatch incombination with a camera. In such configurations, the respective piecesof the physiological data can be separately received by the server, thencombined and analyzed by the server.

The server also receives, from a computing device, location data of theuser (604). This location data can be based on GPS (Global PositioningSystem), telecommunication triangulation, IP (Internet Protocol)address, etc. The location data can also identify location types, suchas an office location, a gymnasium location, a home location, a kitchenlocation, a park location, etc. That is, the location information may bebased on a coordinate-based, latitude/longitude system, or may be basedon a location-type classification system.

The server sends a request to a historical database for historical userdata for the user (606). The historical user data can include, forexample, previous actions of the user, previous stimuluses (content)provided to the user, previous multi-dimensional user states of the userwhich were identified by the server, previous user states of the userwhich were identified by the user (for example, the users themselves canprovide a status), previous physiological data, previous location data,or any other past data about the user. In some cases, the previousphysiological data and the previous user actions can directly correspondto times when the previous content was being provided to the user, andthe previous multi-dimensional user states of the user can be based onthat previous content and previous physiological data. Often, thehistorical database can include a large amount of information, and therequest can be for specific portions of that stored information. Theserver then receives, from the historical database, the historical userdata (608).

The server generates, via a processor, and based on the physiologicaldata, the location data, and the historical user data, amulti-dimensional user state of the user, the multi-dimensional userstate having a plurality of dimensions, where at least one dimension inthe plurality of dimensions is time (610). For example, themulti-dimensional user state can have two or more dimensions for anysingle moment in time, such that with time added the ability for humanbeings to interpret the data clearly becomes impossible. The dimensionsfor a multi-dimensional mental state of the user can include, forexample, autonomy, relatedness, and mastery, as well as relationalvalues identifying relationships between the dimensions over time. Forother multi-dimensional user states, the multi-dimensional user statecan be a physiological user state with multiple dimensions over time, ora combination of mental and physiological user states. In yet otherembodiments, the multi-dimensional mental state can use data which isboth objective and subjective, and identify how the data changes acrosstime and/or other factors.

The server sends, to a content database, a request for a list ofavailable content (612), and receives, from the content database, thelist of available content (614). Exemplary content can includeexercise/movement suggestions, advertisements, songs, television showsor movies, or any other content which can be stored in a database andconsumed or used by a user. The system then ranks, using themulti-dimensional user state, content within the list of availablecontent based on a comparison to the multi-dimensional user state,resulting in a ranked list of available content (616). In other words,the system is determining what content would rank highest for the userbased on how the user's user state has evolved over time in reaction tospecific content, circumstances, and location in conjunction with otherfactors, such as the user's physiological and/or mental state.

In part, this determination requires knowledge of what user state theuser would like to be in in the future. For example, if the user is sad,the user may wish to remain sad, which will dictate different contentthan if the user wishes to become happy. Likewise, if the user feelslethargic, the system needs to be able to determine if the user is sick(and needs to rest) or if the user needs to become motivated to action.Making this determination can be based on previous user behavior. Forexample, the last time the multi-dimensional user state appeared as itcurrently appears can be used to determine what the user was trying todo, and the system can seek to augment that activity. Likewise, thisdetermination can be based on what other users, similar to the user,have attempted to do in the past. For example, if the user is anoptimist, the system can rank the content based on how other optimistshave responded in the past. Similarly, if the user is an athlete, thesystem can rank the content based on how similar athletes haveresponded. Determination of similar groups can be based on user behaviorover time, demographics, education, geographic location, psychologicalprofile, physiological profile, etc. In some configurations, multiplerecommendations can be provided to the user and the user can select (orignore) content from those recommendations. In other configurations, thesystem can provide a single recommendation, which the user can select orignore.

The system then transmits, to the computing device, a suggestioncomprising at least a top-ranked item within the ranked list ofavailable content (618). In other configurations, the system can send asignal modifying content on a user interface based on the ranking.

In some configurations, the method can further include: receivingkeystroke data from a keyboard of the computing device and receivingmouse movement data from a mouse of the computing device, where thegenerating of the multi-dimensional user state is further based on thekeystroke data and the mouse movement data.

In some configurations, the method can further include: receivingtactile data from a touchscreen of the computing device, where thegenerating of the multi-dimensional user state is further based on thetactile data.

In some configurations, the historical user data can include a userprofile of the user, the user profile classifying the user as havinguser states similar to those of a group of other users, where generatingthe multi-dimensional user state of the user is further based on how theuser and the group of other users have previously reacted in similarcircumstances.

In some configurations, the generating of the multi-dimensional userstate can include identifying correlations over time between the changesin the physiological data, the location data, and the historical data.In such configurations, the ranking of the content can be further basedon the correlations over time.

In some configurations, generating of the multi-dimensional user stateis further based on (1) behavioral data of the user, and (2)psychological data of the user. Behavioral data can, for example,include specific reactions to stimuli. Exemplary psychological data caninclude quantifiable objective and/or subjective data regarding thepsychological state of the user.

With reference to FIG. 7, an exemplary system includes a general-purposecomputing device 700, including a processing unit (CPU or processor) 720and a system bus 710 that couples various system components includingthe system memory 730 such as read-only memory (ROM) 740 and randomaccess memory (RAM) 750 to the processor 720. The system 700 can includea cache of high-speed memory connected directly with, in close proximityto, or integrated as part of the processor 720. The system 700 copiesdata from the memory 730 and/or the storage device 760 to the cache forquick access by the processor 720. In this way, the cache provides aperformance boost that avoids processor 720 delays while waiting fordata. These and other modules can control or be configured to controlthe processor 720 to perform various actions. Other system memory 730may be available for use as well. The memory 730 can include multipledifferent types of memory with different performance characteristics. Itcan be appreciated that the disclosure may operate on a computing device700 with more than one processor 720 or on a group or cluster ofcomputing devices networked together to provide greater processingcapability. The processor 720 can include any general purpose processorand a hardware module or software module, such as module 1 762, module 2764, and module 3 766 stored in storage device 760, configured tocontrol the processor 720 as well as a special-purpose processor wheresoftware instructions are incorporated into the actual processor design.The processor 720 may essentially be a completely self-containedcomputing system, containing multiple cores or processors, a bus, memorycontroller, cache, etc. A multi-core processor may be symmetric orasymmetric.

The system bus 710 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 740 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 700, such as during start-up. The computing device 700further includes storage devices 760 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 760 can include software modules 762, 764, 766 forcontrolling the processor 720. Other hardware or software modules arecontemplated. The storage device 760 is connected to the system bus 710by a drive interface. The drives and the associated computer-readablestorage media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputing device 700. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangiblecomputer-readable storage medium in connection with the necessaryhardware components, such as the processor 720, bus 710, display 770,and so forth, to carry out the function. In another aspect, the systemcan use a processor and computer-readable storage medium to storeinstructions which, when executed by the processor, cause the processorto perform a method or other specific actions. The basic components andappropriate variations are contemplated depending on the type of device,such as whether the device 700 is a small, handheld computing device, adesktop computer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk760, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 750, and read-only memory (ROM) 740, may also be used in theexemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 700, an inputdevice 790 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 770 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 700. The communications interface 780generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Use of language such as “at least one of X, Y, and Z” or “at least oneor more of X, Y, or Z” are intended to convey a single item (just X, orjust Y, or just Z) or multiple items (i.e., {X and Y}, {Y and Z}, or {X,Y, and Z}). “At least one of” is not intended to convey a requirementthat each possible item must be present.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

We claim:
 1. A method comprising: receiving, at a server from a sensor,physiological data captured by the sensor from a user; receiving, at theserver from a computing device, location data of the user; sending arequest to a historical database for historical user data for the user;receiving, from the historical database, the historical user data;generating, via a processor of the server, and based on thephysiological data, the location data, and the historical user data, amulti-dimensional user state of the user, the multi-dimensional userstate having a plurality of dimensions, where at least one dimension inthe plurality of dimensions is time; sending, from the server to acontent database, a request for a list of available content; receiving,at the server from the content database, the list of available content;ranking, via the processor of the server and using the multi-dimensionaluser state, content within the list of available content based on acomparison to the multi-dimensional user state, resulting in a rankedlist of available content; and transmitting, from the server to thecomputing device, a suggestion comprising at least a top-ranked itemwithin the ranked list of available content.
 2. The method of claim 1,wherein the sensor is a component of a wearable device.
 3. The method ofclaim 2, wherein: the wearable device is a smartwatch worn by the user;the sensor is a component of the smartwatch; and the physiological datacaptured by the sensor comprises a heartbeat of the user captured by thesmartwatch.
 4. The method of claim 1, wherein: the sensor is a camera;and the physiological data captured by the sensor comprises facialmovement of the user captured by the camera.
 5. The method of claim 1,further comprising: receiving keystroke data from a keyboard of thecomputing device; and receiving mouse movement data from a mouse of thecomputing device, wherein the generating of the multi-dimensional userstate is further based on the keystroke data and the mouse movementdata.
 6. The method of claim 1, further comprising: receiving tactiledata from a touchscreen of the computing device, wherein the generatingof the multi-dimensional user state is further based on the tactiledata.
 7. The method of claim 1, wherein the historical user datacomprises: previous content provided to the user; previous physiologicaldata of the user while the previous content was provided to the user;previous user actions made by the user while the previous content wasprovided to the user; and previous multi-dimensional user states of theuser based on the previous content and the previous physiological data.8. The method of claim 1, wherein: the historical user data comprises auser profile of the user, the user profile classifying the user ashaving user states similar to those of a group of other users; andgenerating the multi-dimensional user state of the user is further basedon how the user and the group of other users have previously reacted insimilar circumstances.
 9. The method of claim 1, wherein themulti-dimensional user state comprises a multi-dimensional mental stateof the user over time.
 10. The method of claim 1, wherein the generatingof the multi-dimensional user state further comprises identifyingcorrelations over time between the changes in the physiological data,the location data, and the historical data, and wherein the ranking ofthe content is further based on the correlations over time.
 11. Themethod of claim 1, wherein the generating of the multi-dimensional userstate is further based on (1) behavioral data of the user, and (2)psychological data of the user.
 12. A system, comprising: a processor;and a non-transitory computer-readable storage medium havinginstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: receiving, from a sensor,physiological data captured by the sensor from a user; receiving, from acomputing device, location data of the user; sending a request to ahistorical database for historical user data for the user; receiving,from the historical database, the historical user data; generating,based on the physiological data, the location data, and the historicaluser data, a multi-dimensional user state of the user, themulti-dimensional user state having a plurality of dimensions, where atleast one dimension in the plurality of dimensions is time; sending, toa content database, a request for a list of available content;receiving, from the content database, the list of available content;ranking content within the list of available content based on acomparison to the multi-dimensional user state, resulting in a rankedlist of available content; and transmitting, to the computing device, asuggestion comprising at least a top-ranked item within the ranked listof available content.
 13. The system of claim 12, wherein the sensor isa component of a wearable device.
 14. The system of claim 13, wherein:the wearable device is a smartwatch worn by the user; the sensor is acomponent of the smartwatch; and the physiological data captured by thesensor comprises a heartbeat of the user captured by the smartwatch. 15.The system of claim 12, wherein: the sensor is a camera; and thephysiological data captured by the sensor comprises facial movement ofthe user captured by the camera.
 16. The system of claim 12, thenon-transitory computer-readable storage medium having additionalinstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: receiving keystroke datafrom a keyboard of the computing device; and receiving mouse movementdata from a mouse of the computing device, wherein the generating of themulti-dimensional user state is further based on the keystroke data andthe mouse movement data.
 17. The system of claim 12, the non-transitorycomputer-readable storage medium having additional instructions storedwhich, when executed by the processor, cause the processor to performoperations comprising: receiving tactile data from a touchscreen of thecomputing device, wherein the generating of the multi-dimensional userstate is further based on the tactile data.
 18. The system of claim 12,wherein the historical user data comprises: previous content provided tothe user; previous physiological data of the user while the previouscontent was provided to the user; previous user actions made by the userwhile the previous content was provided to the user; and previousmulti-dimensional user states of the user based on the previous contentand the previous physiological data.
 19. The system of claim 12,wherein: the historical user data comprises a user profile of the user,the user profile classifying the user as having user states similar tothose of a group of other users; and generating the multi-dimensionaluser state of the user is further based on how the user and the group ofother users have previously reacted in similar circumstances.
 20. Anon-transitory computer-readable storage medium having instructionsstored which, when executed by the processor, cause the processor toperform operations comprising: receiving quantified subjective dataassociated with a user; receiving, from a computing device, locationdata of the user; sending a request to a historical database forhistorical user data for the user; receiving, from the historicaldatabase, the historical user data; generating, based on the quantifiedsubjective data, the location data, and the historical user data, amulti-dimensional user state of the user, the multi-dimensional userstate having a plurality of dimensions, where at least one dimension inthe plurality of dimensions is time; sending, to a content database, arequest for a list of available content; receiving, from the contentdatabase, the list of available content; ranking content within the listof available content based on a comparison to the multi-dimensional userstate, resulting in a ranked list of available content; andtransmitting, to the computing device, a suggestion comprising at leasta top-ranked item within the ranked list of available content.